AvgPool1d

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AvgPool1d#

class brainstate.nn.AvgPool1d(kernel_size, stride=1, padding='VALID', channel_axis=-1, name=None, in_size=None)#

Applies a 1D average pooling over an input signal composed of several input planes.

In the simplest case, the output value of the layer with input size \((N, L, C)\), output \((N, L_{out}, C)\) and kernel_size \(k\) can be precisely described as:

\[\text{out}(N_i, l, C_j) = \frac{1}{k} \sum_{m=0}^{k-1} \text{input}(N_i, \text{stride} \times l + m, C_j)\]

If padding is non-zero, then the input is implicitly zero-padded on both sides for padding number of points.

Shape:

  • Input: \((N, L_{in}, C)\) or \((L_{in}, C)\).

  • Output: \((N, L_{out}, C)\) or \((L_{out}, C)\), where

    \[L_{out} = \left\lfloor \frac{L_{in} + 2 \times \text{padding} - \text{kernel\_size}}{\text{stride}} + 1\right\rfloor\]
Parameters:

  • kernel_size (int | Sequence[int] | integer | Sequence[integer]) – An integer, or a sequence of integers defining the window to reduce over.

  • stride (int | Sequence[int]) – An integer, or a sequence of integers, representing the inter-window stride. Default: 1

  • padding (str | int | Tuple[int, ...] | Sequence[Tuple[int, int]]) – Either the string ‘SAME’, the string ‘VALID’, or a sequence of n (low, high) integer pairs that give the padding to apply before and after each spatial dimension. Default: ‘VALID’

  • channel_axis (int | None) – Axis of the spatial channels for which pooling is skipped. If None, there is no channel axis. Default: -1

  • name (str | None) – The object name.

  • in_size (int | Sequence[int] | integer | Sequence[integer] | None) – The shape of the input tensor.

Examples

>>> import brainstate
>>> # pool with window of size=3, stride=2
>>> m = AvgPool1d(3, stride=2)
>>> input = brainstate.random.randn(20, 50, 16)
>>> m(input).shape
(20, 24, 16)
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